With the explosive growth of datasets’ scale and the inevitable curse of dimensionality, MIPS is essential to offer the scalable services.
However, the HadamardMLP decoders are nonlinear and there do not exist the well studied sublinear complexity algorithms to find the top scoring neighbors for HadamardMLP [1]}.
In this work, we utilize the well studied approximate MIPS techniques with the adaptively adjusted query embeddings to find the top scoring neighbors for the MLP decoders in a progressive manner.
Our method supports the plug-and-play use during inference and significantly acclerates the LP inference with the effective MLP decoders.
